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Home > Standards & Guidances > Methodological Guide

ENCePP Guide on Methodological Standards in Pharmacoepidemiology

 

4.3.5. Disease registries in regulatory practice and health technology assessment

Annex 1 of Module VIII of the Good pharmacovigilance practice provides guidance on use of patient registries for regulatory purpose. It emphasises that the choice of the registry population and the design of the registry should be driven by its objective(s) in terms of outcomes to be measured and analyses and comparisons to be performed. As existing disease registries gather insights into the natural history and clinical aspects of diseases and allow comparison of outcomes between different treatments prescribed for the same indication, they are generally preferred to product registries for regulatory purposes. Module VIII also acknowledges that, due to their observational nature, registries should not normally be used to demonstrate effectiveness in real  world setting, although in some cases (such as rare disease, rare exposure or special population), registries may be the only opportunity to provide insight into effectiveness aspects of a medicinal product. On the other hand, even when efficacy has been demonstrated in randomized clinical trials (RCTs), registries may be useful to study effectiveness in heterogeneous populations and effect modifiers, such as doses that have been prescribed by physicians and that may differ from those used in RCTs, patient sub-groups defined by variables such as age, co-morbidities, use of concomitant medication or genetic factors, or factors related to a defined country or healthcare system that might influence effectiveness.

 

To support better use of existing registries and facilitate the establishment of high-quality new registries, the EU regulatory network developed the Patient registries initiative. As part of this initiative, the ENCePP Resource database of data sources was used to support an inventory of existing disease registries.

 

Incorporating data from clinical practice into the drug development process is a growing interest from health technology assessment (HTA) bodies and payers since reimbursement decisions can benefit from better estimation and prediction of effectiveness of treatments at the time of product launch. An example of where registries can provide clinical practice data is to support the building of predictive models that incorporate data from both RCTs and registries to bridge the efficacy-effectiveness gap, i.e. to generalise results observed in RCTs to a real-world setting. Collecting relevant HTA data in early development and planning post-authorisation data collection may therefore support rapid relative effectiveness assessment and decision-making on drug pricing and reimbursement. In this context, the EUnetHTA Joint Action 3 project has issued guidelines for the definition of the research questions and the choice of data sources and methodology that will support the generation of post-launch evidence.

 

 

 

 

Individual Chapters:

 

1. Introduction

2. Formulating the research question

3. Development of the study protocol

4. Approaches to data collection

4.1. Primary data collection

4.1.1. Surveys

4.1.2. Randomised clinical trials

4.2. Secondary data collection

4.3. Patient registries

4.3.1. Definition

4.3.2. Conceptual differences between a registry and a study

4.3.3. Methodological guidance

4.3.4. Registries which capture special populations

4.3.5. Disease registries in regulatory practice and health technology assessment

4.4. Spontaneous report database

4.5. Social media and electronic devices

4.6. Research networks

4.6.1. General considerations

4.6.2. Models of studies using multiple data sources

4.6.3. Challenges of different models

5. Study design and methods

5.1. Definition and validation of drug exposure, outcomes and covariates

5.1.1. Assessment of exposure

5.1.2. Assessment of outcomes

5.1.3. Assessment of covariates

5.1.4. Validation

5.2. Bias and confounding

5.2.1. Selection bias

5.2.2. Information bias

5.2.3. Confounding

5.3. Methods to handle bias and confounding

5.3.1. New-user designs

5.3.2. Case-only designs

5.3.3. Disease risk scores

5.3.4. Propensity scores

5.3.5. Instrumental variables

5.3.6. Prior event rate ratios

5.3.7. Handling time-dependent confounding in the analysis

5.4. Effect measure modification and interaction

5.5. Ecological analyses and case-population studies

5.6. Pragmatic trials and large simple trials

5.6.1. Pragmatic trials

5.6.2. Large simple trials

5.6.3. Randomised database studies

5.7. Systematic reviews and meta-analysis

5.8. Signal detection methodology and application

6. The statistical analysis plan

6.1. General considerations

6.2. Statistical analysis plan structure

6.3. Handling of missing data

7. Quality management

8. Dissemination and reporting

8.1. Principles of communication

8.2. Communication of study results

9. Data protection and ethical aspects

9.1. Patient and data protection

9.2. Scientific integrity and ethical conduct

10. Specific topics

10.1. Comparative effectiveness research

10.1.1. Introduction

10.1.2. General aspects

10.1.3. Prominent issues in CER

10.2. Vaccine safety and effectiveness

10.2.1. Vaccine safety

10.2.2. Vaccine effectiveness

10.3. Design and analysis of pharmacogenetic studies

10.3.1. Introduction

10.3.2. Identification of generic variants

10.3.3. Study designs

10.3.4. Data collection

10.3.5. Data analysis

10.3.6. Reporting

10.3.7. Clinical practice guidelines

10.3.8. Resources

Annex 1. Guidance on conducting systematic revies and meta-analyses of completed comparative pharmacoepidemiological studies of safety outcomes